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      "#Exploring Click Logs and Testing Statistical Hypotheses\n",
      "\n",
      "For class today, you read a chapter on Exploratory Data Analysis from the book \"Doing Data Science.\" Today, we'll be walking through an analysis of the same data presented in that chapter and extending it with Multiple Hypothesis testing.\n",
      "\n",
      "## Statistical Hypothesis Testing\n",
      "Hypothesis testing is a powerful tool used in statistical analysis to support claims like \"Drug X is better than Drug Y\", or \"Campers who attend a safety course led by a talking bear are effective at preventing forest fires.\" You'll see questions about whether or not an experiment was effective or results from two processes are different all over the place in your data science career.\n",
      "\n",
      "Hypothesis testing allows us to answer these questions with statistical rigor. Generally, we establish a \"null hypothesis\", and then conduct a test which will tell us, given the data, whether or not we can *reject* this null hypothesis. Usually, the null hypothesis is something like \"These two drugs are the *same*.\" or \"This measure's mean is no different than zero.\"\n",
      "\n",
      "We're going to work with one such tool, namely the Student's Two-Sample t-Test. \n",
      "\n",
      "### Historical Anecdote\n",
      "This was not a test designed for students, instead, it was designed by a statistician William Gosset who [published](http://www.york.ac.uk/depts/maths/histstat/student.pdf) under the pseudonym \"Student,\" while working for the Guinness brewing company.\n",
      "\n",
      "### Back to Statistics\n",
      "\n",
      "Student's t-Test is used when comparing samples of normally distributed variables. This assumption of normality is important, but not strict. If the data is very much not normally distributed, a non-parameteric method (that is, distribution agnostic) like the Wilcoxon signed-rank test can be used. We are looking at Welch's t-Test, a variant of the classic Student's test which allows for two samples of different size, and possibly different variance.\n",
      "\n",
      "To perform a t-Test, we compute a \"t statistic\" or \"t score\" with the two samples as input, the formula is:\n",
      "\n",
      "$t = \\frac{mean(X_1) - mean(X_2)}{s_{X_1-X_2}}$\n",
      "\n",
      "where $s_{X_1-X_2} = \\sqrt{\\frac{s^2_1}{n_1} - \\frac{s^2_2}{n_2}}$, and $s_i$ is the sample standard deviation of sample $i$ and $n_i$ is the size of sample $i$.\n",
      "\n",
      "This score comes from a T-distribution, which looks like a normal distribution but with fatter tails. If the two samples are similar, the t-statistic will be close to 0. If they're not, the t-statistic will be high in absolute value. \n",
      "\n",
      "Take a moment and think about the math. Under what conditions is the statistic highest? When the means are very different and their respective sample standard deviations are very small.\n",
      "\n",
      "We'll use Welch's t-Test, an adaptation of the two-sample independent Student's t-test that takes into account samples that may have unequal sizes and variances (which, we can see from our distribution above, may be true in our data set!)\n",
      "\n",
      "Since the t-statistic comes from a statistical distribution, we can map this value to a probability of sampling that value under the null hypothesis. The probability of realizing a high t-statistic when two samples come from the same distribution is very small, thus it's p-value is also small. \n",
      "\n",
      "![T-stat](http://upload.wikimedia.org/wikipedia/commons/4/41/Student_t_pdf.svg)\n",
      "Courtesy of Wikipedia, the probability density function for the Student's t-Distribution. V indicates the number of samples in our distribution.\n",
      "\n",
      "So, the output of a T-test is usually a pair of statistics, the \"t score\", and a \"p value\". The p value has a natural interpretation as a probability. We can say: \"With $100 \\times (1-p)$ percent confidence, I reject the null hypothesis that these two samples are the same.\" Meaning, that when p is very small, the two samples are statistically likely to be different.\n"
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     "source": [
      "##Lab Work\n",
      "\n",
      "##Setup - Install Scipy\n",
      "\n",
      "You'll need to install a python package to your VM using the following command at the shell: `sudo apt-get install python-scipy`\n",
      "\n",
      "##Setup - Data Loading\n",
      "\n",
      "We'll be working with a single day's worth of session log data from the New York Times website. The data is available [here](http://stat.columbia.edu/~rachel/datasets/nyt1.csv) as well as in the class github repo. The data has been nicely cleaned and aggregated for us (no janitor work!) Load up a single day's data into a `DataFrame`, and summarize it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "\n",
      "data = pd.read_csv(\"nyt1.csv\")\n",
      "data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Age</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Impressions</th>\n",
        "      <th>Clicks</th>\n",
        "      <th>Signed_In</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 458441.000000</td>\n",
        "      <td> 458441.000000</td>\n",
        "      <td> 458441.000000</td>\n",
        "      <td> 458441.000000</td>\n",
        "      <td> 458441.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>     29.482551</td>\n",
        "      <td>      0.367037</td>\n",
        "      <td>      5.007316</td>\n",
        "      <td>      0.092594</td>\n",
        "      <td>      0.700930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>     23.607034</td>\n",
        "      <td>      0.481997</td>\n",
        "      <td>      2.239349</td>\n",
        "      <td>      0.309973</td>\n",
        "      <td>      0.457851</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      3.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>     31.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      5.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>     48.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      6.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>    108.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>     20.000000</td>\n",
        "      <td>      4.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                 Age         Gender    Impressions         Clicks  \\\n",
        "count  458441.000000  458441.000000  458441.000000  458441.000000   \n",
        "mean       29.482551       0.367037       5.007316       0.092594   \n",
        "std        23.607034       0.481997       2.239349       0.309973   \n",
        "min         0.000000       0.000000       0.000000       0.000000   \n",
        "25%         0.000000       0.000000       3.000000       0.000000   \n",
        "50%        31.000000       0.000000       5.000000       0.000000   \n",
        "75%        48.000000       1.000000       6.000000       0.000000   \n",
        "max       108.000000       1.000000      20.000000       4.000000   \n",
        "\n",
        "           Signed_In  \n",
        "count  458441.000000  \n",
        "mean        0.700930  \n",
        "std         0.457851  \n",
        "min         0.000000  \n",
        "25%         0.000000  \n",
        "50%         1.000000  \n",
        "75%         1.000000  \n",
        "max         1.000000  \n",
        "\n",
        "[8 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data.shape\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(458441, 5)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Age</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Impressions</th>\n",
        "      <th>Clicks</th>\n",
        "      <th>Signed_In</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 36</td>\n",
        "      <td> 0</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 73</td>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 30</td>\n",
        "      <td> 0</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 49</td>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 47</td>\n",
        "      <td> 1</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "   Age  Gender  Impressions  Clicks  Signed_In\n",
        "0   36       0            3       0          1\n",
        "1   73       1            3       0          1\n",
        "2   30       0            3       0          1\n",
        "3   49       1            3       0          1\n",
        "4   47       1           11       0          1\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that the data contains roughly 450,000 rows, and 5 columns. These columns are Age, Gender, Impressions (the number of pages the user viewed), Clicks (the number of ads a user clicked on), and whether or not the user was signed in.\n",
      "\n",
      "Let's plot the data to get a sense of the distributions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "data.hist(figsize=(10,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x114b9d610>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x114f9f850>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x114fc3d90>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x114fe3d10>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x11542fbd0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x115455f90>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y39DQgOnTp0OlUmHUqFG4fPmy8FpOTg4CAwMRGBiI7du3m1UoxhhjjLGuotPG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\nRnAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114f75f10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What can you say about the distributions of these fields?\n",
      "\n",
      "Some observations.\n",
      "\n",
      "1. Age - looks like a bell curve, but with a bunch of 0 values. How many new york times visitors do you think are 0? Do you think that 0 represents some kind of default value? Maybe some finer granularity plotting will help us here.\n",
      "2. Gender - Ok, it's binary, but would we expect this much of a skew (64/36) in the distribution of genders of readers on sites like the New York Times? Do you think something similar to age is happening here?\n",
      "3. Clicks - looks like it's discrete valued, with the vast majority of the values with 0 clicks.\n",
      "4. Impressions - kind of looks like a bell curve, but not quite - maybe plotting at finer granularity will help.\n",
      "5. Signed In - Looks like another binary variable with more people signed in than not. Can you think of why this might be?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### DIY\n",
      "\n",
      "1. Plot these histograms at finer granularity by setting the number of histogram bins to 50. What kind of distribution does it look like Impressions follows?\n",
      "2. Does the behavior for age look more clear?\n",
      "3. Plot a histogram (or summarize the distribution otherwise - maybe a groupby and a describe?) of gender for sessions where the user is signed in (Signed_In == 1) vs not. Do you notice any differences?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.groupby('Signed_In').Gender.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "Signed_In       \n",
        "0          count    136177.000000\n",
        "           mean          0.000000\n",
        "           std           0.000000\n",
        "           min           0.000000\n",
        "           25%           0.000000\n",
        "           50%           0.000000\n",
        "           75%           0.000000\n",
        "           max           0.000000\n",
        "1          count    319198.000000\n",
        "           mean          0.523644\n",
        "           std           0.499441\n",
        "           min           0.000000\n",
        "           25%           0.000000\n",
        "           50%           1.000000\n",
        "           75%           1.000000\n",
        "           max           1.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Defining New Variables for Analysis\n",
      "\n",
      "Let's create two new columns of interest. The first is Click Thru Rate (CTR), this is a measure commonly used in online advertising to measure the effectiveness of an Ad Campaign. We will use it to measure differences in behavior between groups of users.\n",
      "\n",
      "The second thing we'll do is use pandas' `cut` method to turn a continuous variable (Age), into a discrete one - AgeGroup. This makes things like plotting and measuring differences between groups easier.\n",
      "\n",
      "Before doing any of this, though, we'll copy our data to a new data frame where we remove the cases where there are 0 impressions, because this will cause a divide by zero and possibly bias our analysis. The number of rows affected by this filter is small but non-trivial - these rows may warrant further investigation later!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data1 = data[data.Impressions > 0]\n",
      "data1['CTR'] = data1['Clicks']/data1['Impressions']\n",
      "data1['AgeGroup'] = pd.cut(data1['Age'], [-1,0,18,24,34,44,54,64,1000])\n",
      "data1.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Age</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Impressions</th>\n",
        "      <th>Clicks</th>\n",
        "      <th>Signed_In</th>\n",
        "      <th>CTR</th>\n",
        "      <th>AgeGroup</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 36</td>\n",
        "      <td> 0</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   (34, 44]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 73</td>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> (64, 1000]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 30</td>\n",
        "      <td> 0</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   (24, 34]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 49</td>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   (44, 54]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 47</td>\n",
        "      <td> 1</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   (44, 54]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "   Age  Gender  Impressions  Clicks  Signed_In  CTR    AgeGroup\n",
        "0   36       0            3       0          1    0    (34, 44]\n",
        "1   73       1            3       0          1    0  (64, 1000]\n",
        "2   30       0            3       0          1    0    (24, 34]\n",
        "3   49       1            3       0          1    0    (44, 54]\n",
        "4   47       1           11       0          1    0    (44, 54]\n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exploring New Metrics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data1.shape\n",
      "data1.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(455375, 7)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Age</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Impressions</th>\n",
        "      <th>Clicks</th>\n",
        "      <th>Signed_In</th>\n",
        "      <th>CTR</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 455375.000000</td>\n",
        "      <td> 455375.000000</td>\n",
        "      <td> 455375.000000</td>\n",
        "      <td> 455375.000000</td>\n",
        "      <td> 455375.000000</td>\n",
        "      <td> 455375.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>     29.484010</td>\n",
        "      <td>      0.367051</td>\n",
        "      <td>      5.041030</td>\n",
        "      <td>      0.093218</td>\n",
        "      <td>      0.700956</td>\n",
        "      <td>      0.018471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>     23.606697</td>\n",
        "      <td>      0.482001</td>\n",
        "      <td>      2.208731</td>\n",
        "      <td>      0.310922</td>\n",
        "      <td>      0.457839</td>\n",
        "      <td>      0.069034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      3.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>     31.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      5.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>     48.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      6.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>    108.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>     20.000000</td>\n",
        "      <td>      4.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "                 Age         Gender    Impressions         Clicks  \\\n",
        "count  455375.000000  455375.000000  455375.000000  455375.000000   \n",
        "mean       29.484010       0.367051       5.041030       0.093218   \n",
        "std        23.606697       0.482001       2.208731       0.310922   \n",
        "min         0.000000       0.000000       1.000000       0.000000   \n",
        "25%         0.000000       0.000000       3.000000       0.000000   \n",
        "50%        31.000000       0.000000       5.000000       0.000000   \n",
        "75%        48.000000       1.000000       6.000000       0.000000   \n",
        "max       108.000000       1.000000      20.000000       4.000000   \n",
        "\n",
        "           Signed_In            CTR  \n",
        "count  455375.000000  455375.000000  \n",
        "mean        0.700956       0.018471  \n",
        "std         0.457839       0.069034  \n",
        "min         0.000000       0.000000  \n",
        "25%         0.000000       0.000000  \n",
        "50%         1.000000       0.000000  \n",
        "75%         1.000000       0.000000  \n",
        "max         1.000000       1.000000  \n",
        "\n",
        "[8 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's plot total impressions and clicks by Age Group and whether or not the user is signed in."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "impressionsByAgeSignIn = data1.groupby(['AgeGroup','Signed_In'])['Clicks'].sum()\n",
      "impressionsByAgeSignIn.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x11e1d0150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TZ86cQYcOHcSdwVCb9LeabE1vMwnIfKvJ1vA2k9fz+eef48iRI/jn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\njs9OuKAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11e799750>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By Now, we understand that we need to treat groups differently. Let's take our data and divide it into CTRs by age group for those users that *have clicked* on something (CTR > 0) **and** are signed in (Signed_In > 0)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loggedInCTRsByAgeGroup = data1[(data1.CTR > 0) & (data1.Signed_In > 0)].groupby('AgeGroup').CTR\n",
      "\n",
      "loggedInCTRsByAgeGroup.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "AgeGroup         \n",
        "(0, 18]     count    2371.000000\n",
        "            mean        0.214738\n",
        "            std         0.122203\n",
        "            min         0.058824\n",
        "            25%         0.142857\n",
        "            50%         0.200000\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "(18, 24]    count    1669.000000\n",
        "            mean        0.203926\n",
        "            std         0.116896\n",
        "            min         0.066667\n",
        "            25%         0.125000\n",
        "            50%         0.166667\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "(24, 34]    count    2870.000000\n",
        "            mean        0.204344\n",
        "            std         0.111438\n",
        "            min         0.066667\n",
        "            25%         0.142857\n",
        "            50%         0.166667\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "(34, 44]    count    3592.000000\n",
        "            mean        0.201586\n",
        "            std         0.112516\n",
        "            min         0.066667\n",
        "            25%         0.142857\n",
        "            50%         0.166667\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "(44, 54]    count    3139.000000\n",
        "            mean        0.202531\n",
        "            std         0.108735\n",
        "            min         0.062500\n",
        "            25%         0.142857\n",
        "            50%         0.166667\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "(54, 64]    count    4337.000000\n",
        "            mean        0.208181\n",
        "            std         0.117601\n",
        "            min         0.062500\n",
        "            25%         0.142857\n",
        "            50%         0.166667\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "(64, 1000]  count    4084.000000\n",
        "            mean        0.208385\n",
        "            std         0.110372\n",
        "            min         0.062500\n",
        "            25%         0.142857\n",
        "            50%         0.166667\n",
        "            75%         0.250000\n",
        "            max         1.000000\n",
        "Length: 56, dtype: float64"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Hypothesis Testing\n",
      "\n",
      "Now that we have several samples of user's click-through behavior. What's a question that we can ask ourselves about these samples?\n",
      "\n",
      "One question we might ask is \"Which groups of users is **most different**?\" Or, more precisely: Are any groups of users different? If any groups are different, which ones are?\n",
      "\n",
      "First, we'll collect our groups as separate lists, then, we'll run a T-Test between each pair of groups. Finally, we'll collect the p-values for each pair of groups into a `DataFrame`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import ttest_ind\n",
      "\n",
      "groups = [s for s in loggedInCTRsByAgeGroup]\n",
      "\n",
      "def run_pairwise_tests(groups):\n",
      "    for g in groups:\n",
      "        for g2 in groups:\n",
      "            if g[0] < g2[0]:\n",
      "                yield g[0], g2[0], ttest_ind(g[1], g2[1], equal_var=False)[1]\n",
      "            \n",
      "testResults = pd.DataFrame(run_pairwise_tests(groups))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### DIY\n",
      "\n",
      "1. The object `testResults` contains the p-value for the T-test between CTR samples of all pairs of age groups in our data set. Using this sample, what pairs of groups are different at the 95% confidence level? Which groups are most likely to be different, according to these p-values?\n",
      "2. What about the other end of the spectrum? Which are least likely to be different? Do these results make intuitive sense to you?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testResults[testResults[2] < 0.05].sort(columns=[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>  (0, 18]</td>\n",
        "      <td>   (34, 44]</td>\n",
        "      <td> 0.000028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>  (0, 18]</td>\n",
        "      <td>   (44, 54]</td>\n",
        "      <td> 0.000121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  (0, 18]</td>\n",
        "      <td>   (24, 34]</td>\n",
        "      <td> 0.001439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  (0, 18]</td>\n",
        "      <td>   (18, 24]</td>\n",
        "      <td> 0.004526</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> (34, 44]</td>\n",
        "      <td> (64, 1000]</td>\n",
        "      <td> 0.007703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> (34, 44]</td>\n",
        "      <td>   (54, 64]</td>\n",
        "      <td> 0.010931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> (44, 54]</td>\n",
        "      <td> (64, 1000]</td>\n",
        "      <td> 0.024254</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> (44, 54]</td>\n",
        "      <td>   (54, 64]</td>\n",
        "      <td> 0.032186</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>  (0, 18]</td>\n",
        "      <td>   (54, 64]</td>\n",
        "      <td> 0.033328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  (0, 18]</td>\n",
        "      <td> (64, 1000]</td>\n",
        "      <td> 0.037107</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "           0           1         2\n",
        "2    (0, 18]    (34, 44]  0.000028\n",
        "3    (0, 18]    (44, 54]  0.000121\n",
        "1    (0, 18]    (24, 34]  0.001439\n",
        "0    (0, 18]    (18, 24]  0.004526\n",
        "17  (34, 44]  (64, 1000]  0.007703\n",
        "16  (34, 44]    (54, 64]  0.010931\n",
        "19  (44, 54]  (64, 1000]  0.024254\n",
        "18  (44, 54]    (54, 64]  0.032186\n",
        "4    (0, 18]    (54, 64]  0.033328\n",
        "5    (0, 18]  (64, 1000]  0.037107\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 108
    }
   ],
   "metadata": {}
  }
 ]
}